Route Recommendation to Facilitate Carpooling
Route Recommendation to Facilitate Carpooling
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DOI：
10.1109/mdm55031.2022.00025
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Christine Bassem;S. Honcharuk;M. Mokbel
中科院分区：
文献类型：
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作者：
Christine Bassem;S. Honcharuk;M. Mokbel
最近，拼车平台一直在努力应对司机供应减少的问题，这对乘客产生了负面影响，使他们遭受了长时间的延误和极高的价格飙升。缓解这些问题的一种方法是服务提供商通过为司机推荐单独策划的路径（不一定是最短的）来促进和协调拼车，以完成他们选择的乘车。在本文中，我们重新设计的权重演变的时间图结构，有效地编码大型动态道路网络的时间乘坐可用性。利用该图结构，我们有效地定义了一个多项式时间最优路线推荐算法，该算法可以增加拼车机会，同时考虑到在这种高度动态的环境中司机和乘客的时空约束。最后，我们使用模拟来证明这些路线建议对驾驶员和乘客体验的有效性。
Recently ride-sharing platforms have struggled with a decreased supply of drivers, which has negatively impacted their passengers, by subjecting them to long delays and extremely high surge prices. An approach for mitigating these problems is for service providers to facilitate and coordinate carpooling via the recommendation of individually curated paths, not necessarily the shortest, for drivers towards completing their chosen rides. In this paper, we redesign the Weight Evolving Temporal graph structure to efficiently encode large dynamic road networks with temporal ride availability. Leveraging that graph structure, we efficiently define a polynomial-time optimal route recommendation algorithm that increases carpooling opportunities, taking into consideration the spatio-temporal constraints of both drivers and rides in such a highly-dynamic setting. Finally, we use simulations to demonstrate the effectiveness of these route recommendations, on both the driver and passenger experience.